1. 1 Enhancing Cellular Multicast Performance Using Ad Hoc Networks Jun Cheol Park (jcpark@cs.utah.edu) Sneha Kumar Kasera (kasera@cs.utah.edu) School of Computing University of Utah

2. 2 Why Multicast In Cellular Networks? Transmitting data from single sender to multiple receivers Why not use shared nature of wireless links? Benefits  Efficient resource management  Emergency communication Base Station

3. 3 Receiver heterogeneity Different, dynamic channel condition in wireless networks Key impediment in multicast deployment Base Station

4. 4 Impact of receiver heterogeneity HDR BCMCS (High Data Rate Broadcast and Multicast Services) – 3G proposed standard Fixed data rate for each service More heterogeneity, much less average throughput

5. 5 Outline Combined Architecture  BCMCS + Ad hoc Ad hoc Paths  Transmission Interference Model Distance-2 Vertex Coloring  MIND2 Routing Algorithm Performance Benefits Summary

6. 6 Combined Architecture proxy Base Station Multicast Members Problematic node802.11 BCMCS Each node has dual interfaces: HDR + Wi-Fi 802.11

7. 7 Architecture UCAN (Unified Cellular and Ad-Hoc Network Architecture): Haiyun Luo, et al. Mobicom’ 03  Unicast only  Considers only HDR downlink condition of proxies Our approach  In the context of multicast  Considers achievable data rate of ad hoc path as well as HDR downlink condition

8. 8 How to find best ad hoc paths Achievable data rate of ad hoc path depends upon transmission interference Transmission interference can be modeled by interference graph  Distance-2 vertex coloring Transmission reduction factor in data rate of ad hoc path  determined by minimum Distance-2 vertex coloring

9. 9 Transmission Interference Model  Minimum number of colors for distance-2 vertex coloring matches with transmission reduction factor of ad hoc path 12435 transmission range receiving range 4-hop ad hoc path

10. 10 Minimum Distance-2 Vertex Coloring Distance-1Distance-2 Δ(G) = 8 where Δ(G) is maximum node degree

11. 11 Minimum Distance-2 Coloring Problem NP-complete Minimum solutions are mostly within upper 5% of Δ(G) + 1 (By A.H. Gebremedhin, 2004) Minimum # of colors is approximated by Δ(G) + 1

12. 12 Effective data rate Achievable data rate of ad hoc path  W/(Δ(G)+1)  W = achievable data rate of one-hop link HDR data rate of proxy p = H p  Min{W/(Δ(G)+1), H p } MIND2 Rouging Algorithm  Find a node that has maximal value of this effective data rate

13. 13 Example UCAN routing MIND2 routing

14. 14 Simulation Setup in ns-2 Implement 3G HDR BCMCS Implement MIND2 routing algorithm Use IEEE 802.11b, 11Mbps Uniform distribution of 100 nodes in a cell # of Multicast members: N=20, 40, and 60

15. 15 Performance Gain Goodput = Achievable throughput

16. 16 Performance Comparison Fluctuated better performance due to instability of UCAN

17. 17 Conclusion & Future work Demonstrated receiver heterogeneity problem Modeled transmission interference using distatance-2 vertex coloring Developed an efficient routing algorithm, MIND2 Showed performance benefits of MIND2 Issues for future work  Transmission interference model when links are lossy  Use of ad hoc multicast

18. 18 Thanks! Any questions?

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20. 20 Backup

21. 21 More Optimization Techniques Simple merge  If neighbor vk already has proxy p(vk), examine the value of H p(vk) One more lookahead  T vk = Min {rW/(Δ(G vk )+1), H2 p(vk) }

22. 22 Transmission interference on two ad hoc paths Distance-2 Coloring: 5 colors required

23. 23 Transmission Sequence